Integrating Artificial Intelligence with SAP ERP: ML, NLP, and Disease Analytics for Healthcare, Finance, and Agriculture
DOI:
https://doi.org/10.15662/IJEETR.2024.0606011Keywords:
SAP S/4HANA, Enterprise Resource Planning, Artificial Intelligence, Machine Learning, Natural Language Processing, Disease Analytics, Healthcare, Finance, Agriculture, Predictive Analytics, Intelligent ERPAbstract
The convergence of Artificial Intelligence (AI) and Enterprise Resource Planning (ERP) systems promises to reshape how organizations manage complex, data‑intensive operations across sectors. This paper explores the integration of Machine Learning (ML), Natural Language Processing (NLP), and domain‑specific analytics (particularly disease analytics) into SAP S/4HANA — one of the world’s leading ERP platforms — to support enhanced decision‑making in three critical domains: healthcare, finance, and agriculture. We discuss how embedding ML models within SAP enables predictive analytics for disease diagnosis and treatment protocols, real‑time financial forecasting, anomaly and fraud detection, and agriculture supply‑chain & crop‑yield forecasting. Our methodology combines a conceptual framework for AI‑ERP integration with hypothetical and pilot use cases across sectors. The results suggest that AI‑enhanced ERP can significantly improve operational efficiency, accuracy of predictions, and responsiveness — but also highlight challenges such as data quality, privacy, and system complexity. We conclude by outlining a roadmap for future work and practical deployment, emphasizing governance, domain‑specific customization, and hybrid human–AI collaboration. This study aims to guide researchers and practitioners seeking to leverage AI within ERP ecosystems for multi‑sector impact.
References
1. Narne, H. (2022). AI and Machine Learning in Enterprise Resource Planning: Empowering Automation, Performance, and Insightful Analytics. International Journal of Research and Analytical Reviews (IJRAR). (IJRAR)
2. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
3. Ramakrishna, S. (2022). AI-augmented cloud performance metrics with integrated caching and transaction analytics for superior project monitoring and quality assurance. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5647–5655. https://doi.org/10.15662/IJEETR.2022.0406005
4. Nagarajan, G. (2022). An integrated cloud and network-aware AI architecture for optimizing project prioritization in healthcare strategic portfolios. International Journal of Research and Applied Innovations, 5(1), 6444–6450. https://doi.org/10.15662/IJRAI.2022.0501004
5. Sivaraju, P. S. (2023). Thin client and service proxy architectures for real-time staffing systems in distributed operations. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(6), 9510-9515.
6. “Machine learning driven optimization of enterprise resource planning (ERP) systems: a comprehensive review.” (2023). Beni Suef University Journal of Basic and Applied Sciences. (SpringerLink)
7. “Harnessing Artificial Intelligence and Machine Learning for Cognitive Enterprise Resource Planning.” (2024). International Journal of Computer Science and Engineering Research and Development (IJCSERD), 14(2), 89–101. (ijcserd.com)
8. Kurkute, M. V., Ratnala, A. K., & Pichaimani, T. (2023). AI-powered IT service management for predictive maintenance in manufacturing: leveraging machine learning to optimize service request management and minimize downtime. Journal of Artificial Intelligence Research, 3(2), 212-252.
9. Thangavelu, K., Panguluri, L. D., & Hasenkhan, F. (2022). The Role of AI in Cloud-Based Identity and Access Management (IAM) for Enterprise Security. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 2, 36-72.
10. Raj, A. A., & Sugumar, R. (2023, May). Multi-Modal Fusion of Deep Learning with CNN based COVID-19 Detection and Classification Combining Chest X-ray Images. In 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 569-575). IEEE.
11. Anand, L., Tyagi, R., Mehta, V. (2024). Food Recognition Using Deep Learning for Recipe and Restaurant Recommendation. In: Bhateja, V., Lin, H., Simic, M., Attique Khan, M., Garg, H. (eds) Cyber Security and Intelligent Systems. ISDIA 2024. Lecture Notes in Networks and Systems, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-97-4892-1_23
12. Zubair, K. M., Akash, T. R., & Chowdhury, S. A. (2023). Autonomous Threat Intelligence Aggregation and Decision Infrastructure for National Cyber Defense. Frontiers in Computer Science and Artificial Intelligence, 2(2), 26-51.
13. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
14. Vijayaboopathy, V., Ananthakrishnan, V., & Mohammed, A. S. (2020). Transformer-Based Auto-Tuner for PL/SQL and Shell Scripts. Journal of Artificial Intelligence & Machine Learning Studies, 4, 39-70.
15. Uddandarao, D. P. (2024). Improving Employment Survey Estimates in Data-ScarceRegions Using Dynamic Bayesian Hierarchical Models: Addressing Measurement Challenges in Developing Countries. Panamerican Mathematical Journal, 34(4), 2024.
16. “An Improved Enterprise Resource Planning System Using Machine Learning for Financial ERP Systems.” (year ?; accessible via SCIRP) — this addresses ML for ERP in finance. (SCIRP)
17. Mani, R. (2024). Smart Resource Management in SAP HANA: A Comprehensive Guide to Workload Classes, Admission Control, and System Optimization through Memory, CPU, and Request Handling Limits. International Journal of Research and Applied Innovations, 7(5), 11388-11398.
18. Navandar, Pavan. "Enhancing Cybersecurity in Airline Operations through ERP Integration: A Comprehensive Approach." Journal of Scientific and Engineering Research 5, no. 4 (2018): 457-462.
19. Md Al Rafi. (2022). Intelligent Customer Segmentation: A Data- Driven Framework for Targeted Advertising and Digital Marketing Analytics. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7417–7428.
20. Karim, A. S. A. (2024). Integrating Artificial Intelligence into Automotive Functional Safety: Transitioning from Quality Management to ASIL-D for Safer Future Mobility. The American Journal of Applied Sciences, 6(11), 24-36. https://www.researchgate.net/profile/Abdul-Salam-Abdul-Karim/publication/397636463_Integrating_Artificial_Intelligence_into_Automotive_Functional_Safety_Transitioning_from_Quality_Management_to_ASIL-D_for_Safer_Future_Mobility/links/691812ee1bb5f2388c1e8338/Integrating-Artificial-Intelligence-into-Automotive-Functional-Safety-Transitioning-from-Quality-Management-to-ASIL-D-for-Safer-Future-Mobility.pdf
21. Kumar, R. K. (2023). Cloud-integrated AI framework for transaction-aware decision optimization in agile healthcare project management. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(1), 6347–6355. https://doi.org/10.15680/IJCTECE.2023.0601004
22. Muthusamy, M. (2022). AI-Enhanced DevSecOps architecture for cloud-native banking secure distributed systems with deep neural networks and automated risk analytics. International Journal of Research Publication and Engineering Technology Management, 6(1), 7807–7813. https://doi.org/10.15662/IJRPETM.2022.0506014
23. Suchitra, R. (2023). Cloud-Native AI model for real-time project risk prediction using transaction analysis and caching strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8006–8013. https://doi.org/10.15662/IJRPETM.2023.0601002
24. Kanumarlapudi, P. K., Peram, S. R., & Kakulavaram, S. R. (2024). Evaluating Cyber Security Solutions through the GRA Approach: A Comparative Study of Antivirus Applications. International Journal of Computer Engineering and Technology (IJCET), 15(4), 1021-1040.
25. Archana, R., & Anand, L. (2023, September). Ensemble Deep Learning Approaches for Liver Tumor Detection and Prediction. In 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 325-330). IEEE.
26. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.
27. Kandula N (2023). Gray Relational Analysis of Tuberculosis Drug Interactions A Multi-Parameter Evaluation of Treatment Efficacy. J Comp Sci Appl Inform Technol. 8(2): 1-10.
28. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
29. Udayakumar, R., Chowdary, P. B. K., Devi, T., & Sugumar, R. (2023). Integrated SVM-FFNN for fraud detection in banking financial transactions. Journal of Internet Services and Information Security, 13(3), 12-25.
30. The broader field of expert systems / AI-driven design automation as precedent: for instance, early expert system tools like VP-Expert (late 1980s), which represent early AI attempts at enterprise decision support. (Wikipedia)
